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| Topics Covered in MapReduce | Price (USD) | Time |
|---|---|---|
| MapReduce Basics | $50 | 2 days |
| Hadoop MapReduce Framework | $80 | 3 days |
| MapReduce Algorithms | $100 | 4 days |
| Data Partitioning in MapReduce | $70 | 3 days |
| MapReduce Optimization Techniques | $120 | 5 days |
| MapReduce and Big Data Analytics | $150 | 7 days |
| MapReduce Job Monitoring | $90 | 4 days |
- MapReduce Assignment Help
- Word Frequency Count Using MapReduce
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Word Frequency Count Using MapReduce
from collections import defaultdict
from functools import reduce
# Sample input data
words = ["apple", "banana", "apple", "orange", "banana", "apple"]
# Mapper function
def mapper(word):
return (word, 1)
# Reducer function
def reducer(result, item):
result[item[0]] += item[1]
return result
# Map step
mapped_pairs = map(mapper, words)
# Shuffle and sort step (skipped in this example)
# Reduce step
word_counts = reduce(reducer, mapped_pairs, defaultdict(int))
# Print word counts
for word, count in word_counts.items():
print(f"{word}: {count}")
In this example:
- Mapper function (mapper): Converts each word into a key-value pair where the word itself is the key and the value is initialized to 1.
- Reducer function (reducer): Aggregates the counts of each word by summing up the values for each key.(mappers and reducers are main map functions)
- Map step: Applies the mapper function to each word in the input files (words).
- Reduce step: Applies the reducer function to the mapped pairs to compute the final word counts in output file .
This example demonstrates the basic principles of the MapReduce paradigm for counting word frequencies.
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